基于改进蜻蜓算法的斗轮取料机多目标优化

doi:10.3901/JME.2021.06.211

机械工程学报 ›› 2021, Vol. 57 ›› Issue (6): 211-223.doi: 10.3901/JME.2021.06.211

扫码分享

基于改进蜻蜓算法的斗轮取料机多目标优化

原永亮^1,2, 郭正刚², 王鹏³, 宋学官²

1. 河南理工大学机械与动力工程学院焦作 454003;
2. 大连理工大学机械工程学院大连 116024;
3. 大连华锐重工集团股份有限公司大连 116013

收稿日期:2021-01-10 修回日期:2021-03-02 出版日期:2021-03-20 发布日期:2021-05-25
通讯作者: 宋学官(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为多学科耦合建模与协同优化、代理模型技术、工业大数据挖掘、数字孪生与装备智能化等。E-mail:sxg@dlut.edu.cn
作者简介:原永亮，男， 1989 年出生，博士，讲师。主要研究方向为多学科协同优化、群智能优化算法、基于仿真的优化设计。E-mail： yuanyongliang@hpu.edu.cn
基金资助:
国家自然科学基金资助项目(52075068)。

Multi-objective Optimization of Bucket Wheel Reclaimer Based on Improved Dragonfly Algorithm

YUAN Yongliang^1,2, GUO Zhenggang², WANG Peng³, SONG Xueguan²

1. School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003;
2. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
3. Dalian Huarui Heavy Industry Group Co., Ltd. Dalian 116013

Received:2021-01-10 Revised:2021-03-02 Online:2021-03-20 Published:2021-05-25

摘要/Abstract

摘要： 针对斗轮取料机系统能耗高、重量大、制造成本高、设计变量多等特点，提出一种改进的蜻蜓算法用以求解斗轮取料机的多目标优化问题。提出的改进蜻蜓算法基于自然现象和物理现象，采用空气阻力和库仑力混合组成的策略对传统蜻蜓算法进行改进，并通过测试函数验证了改进后蜻蜓算法的性能。然后建立考虑斗轮取料机可靠性和振动频率约束的质量与转动惯量的多目标优化模型，利用改进后的蜻蜓算法进行多目标求解，获得斗轮取料机的Pareto前沿解集，选择以质量与转动惯量合适权重比为例进行优化，验证开展斗轮取料机多目标优化的有效性。结果表明，优化得到的阶梯截面布局方案具有更小的质量和转动惯量值，同时可以有效避开斗轮取料机系统的共振问题，可以使斗轮取料机的性能得到有效改善，为未来的整机材料-结构-控制多学科一体化协同优化提供基础。

关键词: 蜻蜓算法, 混合策略, 斗轮取料机, 多目标优化

Abstract: Aiming at the characteristics of high energy consumption, maximum gross weight, high manufacturing cost and multiple design variables of the bucket wheel reclaimer(BWR), an improved dragonfly algorithm is proposed to solve the multi-objective optimization problem of the BWR. A hybrid strategy composed of air resistance and Coulomb force is proposed to improve the traditional dragonfly algorithm(DA) based on the natural and physical phenomena. The performance of the improved DA is verified by the test functions. A multi-objective optimization model considering the mass and rotational inertia of the BWR's reliability and frequency constraints is established. The improved DA is used for multi-objective solution to obtain the Pareto solution set of the BWR, and a suitable weight ratio of mass and rotational inertia is selected as an example for optimization research. The main purpose is to verify the effectiveness of the multi-objective optimization of the BWR. Results show that the optimized structure layout not only has smaller mass and rotational inertia values, but also can effectively avoid the resonance of the BWR. In addition, it can effectively improve the performance of the BWR, and provide the basis for the future integration of material-structure-control multidisciplinary design optimization.

Key words: dragonfly algorithm, hybrid strategy, bucket wheel rclaimer, multi-objective optimization

中图分类号:

TH122

原永亮, 郭正刚, 王鹏, 宋学官. 基于改进蜻蜓算法的斗轮取料机多目标优化[J]. 机械工程学报, 2021, 57(6): 211-223.

YUAN Yongliang, GUO Zhenggang, WANG Peng, SONG Xueguan. Multi-objective Optimization of Bucket Wheel Reclaimer Based on Improved Dragonfly Algorithm[J]. Journal of Mechanical Engineering, 2021, 57(6): 211-223.

参考文献

[1] FILATOV G. Optimum designing of the bearing structures of bucket wheel excavators. three-mass model[J]. Journal of Emerging Technology and Advanced Engineering,2017,7(10):378-383.
[2] DORN W,GOMORY R,GREENBERG H. Automatic design of optimal structures[J]. Design Mechaniuque,1964,3(1):25-52.
[3] RUSIŃSKI E, MOCZKO P, KACZYŃSKI P. Structural modifications of excavator's bucket wheel by the use of numerical methods[C]//Solid State Phenomena. Trans Tech Publications,2010,165:330-335.
[4] SHANG P,HU Y Z,HE L H,et al. The modal analysis of the main steel structure of bucket wheel stacker reclaimer[C]//Advanced Materials Research. Trans Tech Publications,2013,690:3121-3124.
[5] 金华英.斗轮堆取料机前臂架结构优化与设计分析[J]. 内燃机与配件,2018(23):205-206.JIN Huaying. Structural optimization and design analysis of forearm of bucket wheel stacker reclaimer[J]. Internal Combustion Engine & Parts,2018,690:3121-2124.
[6] YUAN Y L, LÜ L Y, WANG S B,et al. Multidisciplinary co-design optimization of structural and control parameters for bucket wheel reclaimer[J]. Frontiers of Mechanical Engineering,2020,15(3):406-416.
[7] YUAN Y L, LÜ L Y, WANG X B,et al. Optimization of a frame structure using the coulomb force search strategy-based dragonfly algorithm[J]. Engineering Optimization,2020,52(6):915-931.
[8] YUAN Y L,SONG X G,SUN W,et al. Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm[J]. AIP Advances,2018,8 (5):055115.
[9] ZHAO M P. GA-based optimal design of derricking balance mechanism of bucket wheel stacker reclaimer[C]//Advanced Materials Research. Trans Tech Publications,2012,562:672-675.
[10] 钱云芳,蔡岗础. 大型斗轮堆取料机拉杆的受力分析与优化[J]. 港口装卸,2018(6):6-10.QIAN Yunfang,CAI Gangchu. Stress analysis and optimization of drawbar of large bucket wheel stacker reclaimer[J]. Port Operation,2018(6):6-10.
[11] 王九生,王海斌. DQLK1000/1200.30型斗轮堆取料机回转机构改型优化[J]. 酒钢科技,2019(3):53-56.WANG Jiusheng,WANG Haibin. Modification and optimization of slewing mechanism for DQLK 1000/1200.30 bucket wheel stacker reclaim[J]. JISCO Technology,2019(3):53-56.
[12] 吴奋敬. 螺旋卸船机相对旋转式取料装置取料机理及仿真研究[D]. 武汉:武汉理工大学,2017.WU Feijing. Research of reclaiming mechanism and simulation of relatively-rotating inlet device of screw unloader[D]. Wuhan:Wuhan University of Technology,2017.
[13] LIU S Y,LI J,HE H. Topological optimization of the main girder web based on ESO[J]. Journal of Engineering Design,2011,18(3):174-177.[14] SUN W,PENG X,DOU J,et al. Surrogate-based weight reduction optimization of forearm of bucket-wheel stacker reclaimer[J]. Structural and Multidisciplinary Optimization,2020,61(3):1287-1301.
[15] 龚建民. 悬臂斗轮堆取料机总体设计及机构分析与优化设计[J]. 水利水电施工,2016,157(4):60-67.GONG Jianmin. Overall design and mechanism analysis and optimization design of cantilever bucket wheel stacker and reclaimer[J]. Water Conservancy and Hydropower Construction,2016,157(4):60-67.
[16] 李军. 基于散体力学的堆取料机料斗结构优化设计[D]. 天津:河北工业大学,2018.LI Jun. Optimization design of hopper structure of heap reclaimer based on bulk mechanics[D]. Tianjin:Hebei University of Technology. 2018.
[17] WANG X B,SUN W,LI E Y,et al. Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning[J]. Structural and Multidisciplinary Optimization,2018,58(5):1-19.
[18] MIRJALILI S. Dragonfly algorithm:a new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J]. Neural Computing and Applications,2016,27(4):1053-1073.
[19] YANG X S. Nature-inspired metaheuristic algorithms[M]. Beckington:Luniver Press,2008.
[20] PAVLYUKEVICH I. Levy flights,non-local search and simulated annealing[J]. Mathematics,2007,226(2):1830-1844.
[21] 徐遥,王士同. 引力搜索算法的改进[J]. 计算机工程与应用,2011,47(35):188-192.XU Yao,WANG Shitong. Enhanced version of graritaflonal search algorithm:weighted GSA[J]. Computer Engineering and Applications.2011,47(35):188-192.[22] 刘俊彤,王可人,冯辉,等. 一种基于认知引擎的t分布变异萤火虫算法[J]. 数据采集与处理,2015(4):909-914.LIU Juntong,Wang Keren,FENG Hui,et al. GSO algorithm with t-distribution mutation for cognitive engine[J]. Journal of Data Acquisition and Processing,2015 (4):909-914.
[23] MIRJALILI S. Moth-flame optimization algorithm:A novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems,2015,89:228-249.
[24] LI Z,ZHOU Y,ZHANG S,et al. Lévy-flight moth-flame algorithm for function optimization and engineering design problems[J]. Mathematical Problems in Engineering,2016,2016(8):1-22.
[25] IRUTHAYARAJAN M W,BASKAR S. Covariance matrix adaptation evolution strategy based design of centralized PID controller[J]. Expert Systems with Applications,2010,37(8):5775-5781.
[26] SONG J M,LI S P. Elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization[C]//2017 IEEE International Conference on Information and Automation (ICIA). IEEE,2017,1178-1183.
[27] 徐建国. 轴流泵叶片应力与模态分析[D]. 扬州:扬州大学,2008.XU Jianguo. Stress and modal analysis of axial-flow pump blades[D]. Yangzhou:Yangzhou University,2008.
[28] COELLO,C A. An updated survey of GA-based multiobjective optimization techniques[J]. Acm Computing Surveys,2000,32(2):109-143.
[29] YAPO P O,GUPTA H V,SOROOSHIAN S. Multi-objective global optimization for hydrologic models[J]. Journal of Hydrology,1998,204(1-4):83-97.

基于改进蜻蜓算法的斗轮取料机多目标优化

Multi-objective Optimization of Bucket Wheel Reclaimer Based on Improved Dragonfly Algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	周晨, 徐飞翔. 四轮独立转向车辆转向模式切换轨迹多目标优化方法研究[J]. 机械工程学报, 2024, 60(6): 306-320.
[2]	柴依扬, 张乐乐, 窦伟元, 张海峰. 基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化[J]. 机械工程学报, 2024, 60(6): 321-333.
[3]	苏建涛, 董绍华, 朱诗敏. 多目标混合流水车间机器故障重调度问题研究[J]. 机械工程学报, 2024, 60(4): 438-448.
[4]	唐红涛, 魏书鹏, 李西兴, 雷德明, 汪开普. 考虑排队因素的多车型车辆配置与路径协同优化[J]. 机械工程学报, 2024, 60(4): 458-472.
[5]	吕利叶, 鲁玉军, 王硕, 刘印, 李昆鹏, 宋学官. 代理模型技术及其应用：现状与展望[J]. 机械工程学报, 2024, 60(3): 254-281.
[6]	王旭, 姜兴宇, 杨国哲, 孙猛, 于沈弘, 毕凯航, 赵日铮, 刘伟军. 基于PSO-SSA的激光清洗装备人机界面布局优化研究[J]. 机械工程学报, 2024, 60(20): 372-387.
[7]	熊瑞, 李正阳, 燕润博, 杨瑞鑫. 全气候自热锂离子电池及低温极速加热策略研究[J]. 机械工程学报, 2024, 60(18): 247-257.
[8]	梁旭, 张建勇, 李国涛, 苏婷婷, 何广平, 侯增广. 面向骨折复位手术的冗余并联机构：设计、建模与性能分析[J]. 机械工程学报, 2024, 60(17): 133-146.
[9]	王洪亮, 雍健羽, 皮大伟, 谢伯元, 王尔烈, 王显会. 基于贝塞尔曲线和多目标优化方法的多车队形切换轨迹规划[J]. 机械工程学报, 2024, 60(16): 270-279.
[10]	赵峰, 胡伟飞, 李光, 邓晓豫, 刘振宇, 郭云飞, 谭建荣. 基于混合指标自适应采样代理模型的多目标优化设计方法[J]. 机械工程学报, 2024, 60(13): 81-91.
[11]	王娟, 刘美红, 祝世兴, 陈文博, 李遇贤, 孙军锋. 指尖密封结构参数的多目标混合教与学优化方法[J]. 机械工程学报, 2023, 59(9): 157-170.
[12]	张新彤, 张成明, 李立毅, 傅鹏睿. 电推进用高效轻质永磁同步电机的设计方法[J]. 机械工程学报, 2023, 59(8): 181-195.
[13]	刘伟军, 索英祁, 姜兴宇, 田志强, 张栋, 杨国哲, 王弘玥, 韩清冰. 激光清洗过程低碳建模与工艺参数优化[J]. 机械工程学报, 2023, 59(7): 276-294.
[14]	王亚良, 高康洪, 范欣宇, 金寿松. 面向不确定性的车间布局调度集成建模与优化[J]. 机械工程学报, 2023, 59(5): 235-246.
[15]	徐弓岳, 冯泽民, 郭二廓. 正铲液压挖掘机三副摇杆工作机构的超多目标优化设计[J]. 机械工程学报, 2023, 59(3): 54-65.